Method for generating a magnetic resonance image dataset, computer program product, data medium, and magnetic resonance system

Abstract

A method is provided for generating a magnetic resonance image dataset containing spectroscopic information from an in vivo measurement. The method includes acquiring measurement data by a non-Cartesian k-space sampling scheme, performing a gradient correction of the measurement data, regridding the measurement data to Cartesian coordinates, Fourier-transforming the measurement data, determining the fat component and / or the fatty acid components of at least some of the volume elements using a model function applied to the signal distribution, and generating at least one magnetic resonance image dataset in which one piece of the spectroscopic information is mapped in a spatially resolved manner. A computer program product, a data medium, and a magnetic resonance system are also disclosed.

Description

[0001]The present patent document claims the benefit of German Patent Application No. DE 10 2018 202 546.0, filed Feb. 20, 2018, which is also hereby incorporated by reference.TECHNICAL FIELD[0002]The disclosure relates to a method for generating a magnetic resonance image dataset containing spectroscopic information from an in vivo measurement.BACKGROUND[0003]In order to visualize the spatial distribution of different substances or tissues or spin species, it is known to make use of the phenomenon known as chemical shift. This entails supplementing a spectroscopic experiment with phase encoding gradients in order to generate spatial information. These experiments are summarized under the acronym CSI, for chemical shift imaging.[0004]Due to the use of phase gradients in all spatial directions, these methods are very time-intensive.[0005]It is furthermore known to exploit the different phase shifts of fat and water protons in order to overlay the spins additively and subtractively in...

AI Technical Summary

Benefits of technology

The invention is a method for generating a magnetic resonance image dataset with spectroscopic information from an in vivo measurement. The method includes acquiring measurement data using a non-Cartesian k-space sampling scheme, correcting the measurement data to eliminate gradient errors, regridding the measurement data, and Fourier-transforming the measurement data to produce an image dataset. The method also allows mapping of spectroscopic information, such as fat components, in a spatially resolved manner. This can be done by using a model function applied to the signal waveform. The repetition time may be less than 100 milliseconds, minimizing measurement time. The method also allows for the efficient acquisition of calibration data by separating the measurements. This helps to improve the accuracy of the measurement data.

Problems solved by technology

Due to the use of phase gradients in all spatial directions, these methods are very time-intensive.

However, it is not possible in this case to differentiate between the individual components of the fat signal.

As well as general biopsy risks, this has the disadvantage that the result may be distorted by the local sample taking.

A non-alcoholic fatty liver disease may therefore be wrongly detected or rejected by mistake.

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